Tearing down virtual machines implementing parallel operators in a streaming application based on performance

ABSTRACT

A streams manager monitors performance of parallel portions of a streaming application implemented in multiple virtual machines (VMs). When the performance provided by the multiple VMs is no longer needed, one or more of the VMs can be torn down. The performance of the VMs is monitored. When the least performing VM can be torn down, it is torn down. When the least performing VM cannot be torn down, information regarding a better performing VM is gathered, and it is determined whether the least performing VM can be made more similar to the better performing VM. When the least performing VM can be made more similar to the better performing VM, the least performing VM is changed to improve its performance, and the better performing VM is torn down.

BACKGROUND

1. Technical Field

This disclosure generally relates to streaming applications, and morespecifically relates to selecting which of multiple virtual machines(VMs) that implement parallel operators in a streaming applicationshould be torn down based on performance of the operators in the VMs.

2. Background Art

Streaming applications are known in the art, and typically includemultiple operators coupled together in a flow graph that processstreaming data in near real-time. An operator typically takes instreaming data in the form of data tuples, operates on the tuples insome fashion, and outputs the processed tuples to the next operator.Streaming applications are becoming more common due to the highperformance that can be achieved from near real-time processing ofstreaming data.

Many streaming applications require significant computer resources, suchas processors and memory, to provide the desired near real-timeprocessing of data. However, the workload of a streaming application canvary greatly over time. Allocating on a permanent basis computerresources to a streaming application that would assure the streamingapplication would always function as desired (i.e., during peak demand)would mean many of those resources would sit idle when the streamingapplication is processing a workload significantly less than itsmaximum. Furthermore, what constitutes peak demand at one point in timecan be exceeded as the usage of the streaming application increases. Fora dedicated system that runs a streaming application, an increase indemand may require a corresponding increase in hardware resources tomeet that demand.

Systems have been developed to dynamically increase the performance of astreaming application by creating parallel paths of operators that areimplemented in multiple virtual machines (VMs). Once there are multipleparallel paths of operators in different VMs, should it be determinedthat the processing capability of all of the parallel paths is no longerneeded, one of more of the VMs can be torn down. One solution would beto tear down the VM that was last created. However, this could result intearing down a parallel path that is performing better than anotherparallel path.

BRIEF SUMMARY

A streams manager monitors performance of parallel portions of astreaming application implemented in multiple virtual machines (VMs).When the performance provided by the multiple VMs is no longer needed,one or more of the VMs can be torn down. The performance of the VMs ismonitored. When the least performing VM can be torn down, it is torndown. When the least performing VM cannot be torn down, informationregarding a better performing VM is gathered, and it is determinedwhether the least performing VM can be made more similar to the betterperforming VM. When the least performing VM can be made more similar tothe better performing VM, the least performing VM is changed to improveits performance, and the better performing VM is torn down. When theleast performing VM cannot be made more similar to the better performingVM, if the least performing VM can be migrated to a location where theleast performing VM can be made more similar to the better performingVM, the least performing VM is migrated, changed to improve itsperformance, and the better performing VM is torn down. When the leastperforming VM cannot be made more similar to the better performing VM,and when the least performing VM cannot be migrated, the betterperforming VM is torn down. When a VM is torn down, the performance datarelating to the VM and other VMs that implement parallel paths is loggedto enhance the likelihood that a VM can be created that will be retainedin the future.

The foregoing and other features and advantages will be apparent fromthe following more particular description, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The disclosure will be described in conjunction with the appendeddrawings, where like designations denote like elements, and:

FIG. 1 is a block diagram of a cloud computing node;

FIG. 2 is a block diagram of a cloud computing environment;

FIG. 3 is a block diagram of abstraction model layers;

FIG. 4 is a block diagram showing some features of a cloud manager;

FIG. 5 is a block diagram showing some features of a streams manager;

FIG. 6 is a flow diagram of a method for a streams manager to manage astreaming application by tearing down one of multiple parallel paths ina flow graph implemented in different VMs;

FIG. 7 is a block diagram of one specific example of a streamingapplication; and

FIG. 8 is a flow diagram of a method for creating a VM based on loggedperformance data.

DETAILED DESCRIPTION

The disclosure and claims herein relate to a streams manager thatmonitors performance of parallel portions of a streaming applicationimplemented in multiple virtual machines (VMs). When the performanceprovided by the multiple VMs is no longer needed, one or more of the VMscan be torn down. The performance of the VMs is monitored. When theleast performing VM can be torn down, it is torn down. When the leastperforming VM cannot be torn down, information regarding a betterperforming VM is gathered, and it is determined whether the leastperforming VM can be made more similar to the better performing VM. Whenthe least performing VM can be made more similar to the betterperforming VM, the least performing VM is changed to improve itsperformance, and the better performing VM is torn down.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, tabletcomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processing unit 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media. An example ofremovable media is shown in FIG. 1 to include a Digital Video Disc (DVD)192.

System memory 130 can include computer system readable media in the formof volatile or non-volatile memory, such as firmware 132. Firmware 132provides an interface to the hardware of computer system/server 110.System memory 130 can also include computer system readable media in theform of volatile memory, such as random access memory (RAM) 134 and/orcache memory 136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, Redundant Array of Independent Disk(RAID) systems, tape drives, data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 in FIG. 2 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM System z systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM System p systems; IBMSystem x systems; IBM BladeCenter systems; storage devices; networks andnetworking components. Examples of software components include networkapplication server software, in one example IBM WebSphere® applicationserver software; and database software, in one example IBM DB2® databasesoftware. IBM, System z, System p, System x, BladeCenter, WebSphere, andDB2 are trademarks of International Business Machines Corporationregistered in many jurisdictions worldwide.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. A cloud manager 350 is representative of a cloudmanager as described in more detail below. While the cloud manager 350is shown in FIG. 3 to reside in the management layer 330, cloud manager350 can span all of the levels shown in FIG. 3, as discussed in detailbelow.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and a streams manager 360, as discussed in more detailbelow.

As will be appreciated by one skilled in the art, aspects of thisdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects of the presentinvention may take the form of a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a non-transitory computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 4 shows one suitable example of the cloud manager 350 shown in FIG.3. The cloud manager 350 includes a cloud provisioning mechanism 410that includes a resource request interface 420. The resource requestinterface 420 allows a software entity, such as the streams manager 360,to request without human intervention virtual machines from the cloudmanager 350 or to tear down specified virtual machines by the cloudmanager 350. The cloud manager 350 also includes a user interface 430that allows a user to interact with the cloud manager to perform anysuitable function, including provisioning of VMs, destruction (tearingdown) of VMs, performance analysis of the cloud, etc. The differencebetween the resource request interface 420 and the user interface 430 isa user must manually use the user interface 430 to perform functionsspecified by the user, while the resource request interface 420 may beused by a software entity to request provisioning or destruction ofcloud resources by the cloud mechanism 350 without input from a humanuser. Of course, cloud manager 350 could include many other features andfunctions known in the art that are not shown in FIG. 4.

FIG. 5 shows one suitable example of the streams manager 360 shown inFIG. 3. The streams manager 360 is software that manages one or morestreaming applications, including creating operators and data flowconnections between operators in a flow graph that represents astreaming application. The streams manager 360 includes a performancemonitor 510 with one or more performance thresholds 520. Performancethresholds 520 can include static thresholds, such as percentage used ofcurrent capacity, and can also include any suitable heuristic formeasuring performance of a streaming application as a whole or formeasuring performance of one or more operators in a streamingapplication. Performance thresholds 520 may include different thresholdsand metrics at the operator level, at the level of a group of operators,and/or at the level of the overall performance of the streamingapplication. The stream performance monitor 510 monitors performance ofa streaming application, and when current performance compared to theone or more performance thresholds 520 indicates current performanceneeds to be improved, the stream performance monitor 510 may communicatethe need for resources to the cloud resource request mechanism 530. Thecloud resource request mechanism 530, in response to the communicationfrom the stream performance monitor, assembles a cloud resource request530, which can include information such as a number of VMs to provision550, stream infrastructure needed in each VM 560, and a streamapplication portion 570 for each VM. Once the cloud resource request 530is formulated, the streams manager 360 submits the cloud resourcerequest 530 to a cloud manager, such as cloud manager 350 shown in FIGS.3 and 4.

The cloud resource request can be formatted in any suitable way. Asimple example will illustrate two suitable ways for formatting a cloudresource request. Let's assume the streams manager determines it needstwo VMs, where both have common stream infrastructure, with a first ofthe VMs hosting operator A and the second of the VMs hosting operator B.The cloud resource request 540 in FIG. 5 could specify two VMs at 550,could specify the common stream infrastructure, such as an operatingsystem and middleware, at 560, and could specify operator A and operatorB at 570. In response, the cloud manager would provision two VMs withthe common stream infrastructure, with the first of the VMs hostingoperator A and the second of the VMs hosting operator B. In thealternative, the cloud resource request 540 could be formulated suchthat each VM is specified with its corresponding stream infrastructureand stream application portion. In this configuration, the cloudresource request would specify a first VM with the common streaminfrastructure and operator A, and a second VM with the common streaminfrastructure and operator B.

The streams manager 360 includes a VM tear down mechanism 522. When thestreams manager 360 determines from the performance monitor 510 a VM isno longer needed, the VM tear down mechanism 522 makes a request via theresource request interface 420 of the cloud provisioning mechanism 410to tear down a specified VM. In response, the cloud manager 350 willthen tear down the specified VM. Streams manager 360 also includes a VMperformance log 524. Any suitable performance data regarding one or moreoperators monitored by the streams performance monitor 510 can be loggedin VM performance log 524. In one particular embodiment, when differentVMs are deployed that operate on parallel portions of a flow graph, thecharacteristics of the VMs and their respective performance is logged.This can help in making future decisions when a similar parallel portionof the flow graph needs to be created in a VM by analyzing the VMperformance log 524 to determine a configuration for a VM that had thebest performance in the past. By creating a VM based on a configurationin the VM performance log, it is more likely the VM will perform better.

FIG. 6 shows one suitable example of a method 600 for managing astreaming application. Method 600 begins with a need to tear down eitherVM1 or VM2 (step 610). Method 600 assumes a flow graph has been deployedsuch that parallel portions of the flow graph reside in different VMs.One simple example for parallel portions being deployed to two differentVMs is shown in the flow graph in FIG. 7. A first virtual machine 710and a second virtual machine 720 have similar operators and connectionsbetween operators, and operate on data tuples in parallel. Thus, each VM710 and 720 receives tuples from operator A, and each VM 710 and 720outputs tuples to operator E. Within each VM, the tuples received fromoperator A are processed by operators B and B′, which output theirtuples to respective operators C and C′, which output their tuples torespective operators D and D′, which both output their tuples tooperator E. The tuples output from operator E are sent to operators Fand G. One reason to have a flow graph as shown in FIG. 7 with parallelportions is when a streams operator replicates part of the flow graph toanother VM to improve performance of the streaming application. Thus,the original flow graph might have included A, B, C, D, E, F and G shownin FIG. 7. In response to monitoring the performance of the operators,the streams manager could decide to replicate operators B, C and D in adifferent VM 720 as B′, C′ and D′ to improve performance, resulting inthe configuration shown in FIG. 7.

The example above illustrates one scenario that accounts for creatingparallel portions of a flow graph on different VMs. However, thedisclosure and claims herein extend to any flow graph that includes anysuitable number of operators implemented in parallel on two or more VMs.As used herein, two portions of the flow graph are in parallel if theyimplement the same or similar operators interconnected by similarconnections and if they receive tuples from the same source and outputtuples to the same sink. In one specific implementation, the operatorsin two parallel portions of the flow graph could be identical. Inanother specific implementation, the operators may be similar inperforming an equivalent function in terms of output tuples buy mayperform that function in a different way.

For ease of illustration, method 600 in FIG. 6 assumes two parallel VMsas shown in FIG. 7 that implement parallel portions of the flow graph.Of course, other configurations are possible. For example, there couldbe five different VMs that each implement the same parallel portion ofthe flow graph. Let's assume the streams manager determines from theperformance monitor two of these five VMs are no longer needed. Stepsvery similar to method 600 could then be performed to tear down two ofthe five VMs. These are other variations will be obvious to one ofordinary skill in the art.

When the streams manager determines to tear down either VM1 or VM2 (step610), the streams manager determines which of VM1 and VM2 is leastperforming (step 620), preferably using the stream performance monitor510 shown in FIG. 5. In the case of a streaming application, determiningwhich is least performing could be a simple process of determining therate of processing tuples by each VM. However, least performing does notnecessarily mean the VM that is processing tuples at the lesser rate.For example, an operator could be one that has to provide responsetuples in a set amount of time no matter what. In that case, thethroughput wouldn't indicate which is least performing. Instead, thenumber of tuples ignored by each operator could be a measure of leastperforming, where the operator (or VM) that ignores more tuples is leastperforming. For this simple example in FIG. 6, we assume the rate ofprocessing tuples is an accurate gauge of performance, and furtherassume VM1 processes tuples at a rate less than VM2, so VM1 is leastperforming and VM2 is better performing. A determination is made whetherthe least performing VM can be torn down (step 630). When the leastperforming VM can be torn down (step 630=YES), the least performing VMis torn down (step 632) and the VM performance data for the torn down VMis logged (step 634). Note that step 634 may be optional at the time oftearing down the least performing VM, because the VM performance datafor the least performing VM may have already been logged prior totearing down the least performing VM.

When the least performing VM cannot be torn down (step 630=NO),information is gathered from the better performing VM (step 642). Thisinformation could include, for example, performance data as well asresource allocations for the VM. There are different circumstances thatcan result in not wanting to tear down a least performing VM. Forexample, a VM cannot be torn down (step 630=No) when the leastperforming VM is on a permanently assigned server or when the leastperforming VM has been running longer and is therefore providing moreaccurate results. Of course, there are other circumstance where theleast performing VM cannot be torn down, or should not be torn downbased on any suitable criteria or heuristic. When the least performingVM can be made more similar to the better performing VM (step 650=YES),the least performing VM is changed to improve its performance (step670). The better performing VM is then torn down (step 680). When theleast performing VM cannot be made more similar to the better performingVM (step 650=NO), a determination is made whether the least performingVM can be migrated to be made more similar to the better performing VM(step 660). A simple example will illustrate. Let's assume the leastperforming VM has only half the memory allocated to it than the betterperforming VM, but there is no more memory available to allocate to theleast performing VM. In this case, if the least performing VM ismigrated to a different host that has enough available memory, the leastperforming VM can then be changed by increasing the memory allocated toit. When the least performing VM can be migrated to a differentlocation, such as a physical host, to be made more similar to the betterperforming VM (step 660=YES), the least performing VM is migrated (step662) and changed to improve its performance (step 664). The betterperforming VM is then torn down (step 680). When the least performing VMcannot be migrated to be made more similar to the better performing VM(step 660=NO), the better performing VM is then torn down (step 680) andthe performance data is logged (step 690). Note the logging ofperformance data in step 690 may be optional.

Referring again to FIG. 7, if we assume VM1 is least performing and VM2is better performing, and if we assume VM1 can be torn down, method 610would flow through steps 610, 620 and 630=YES, VM1 will be torn down instep 632, and performance data for VM1 can be optionally logged at 634.

While the simple example in FIG. 7 shows three operators B, C and D thatare implemented in parallel in two separate VMs, this is not to beconstrued as limiting of the concepts herein. Any suitable number ofoperators could be deployed in parallel in any suitable number of VMs.The disclosure and claims herein expressly extend to any number ofvirtual machines that implement any suitable number of operators inparallel in a flow graph of a streaming application.

Referring to FIG. 8, a method 800 can take advantage of thepreviously-logged performance data for VMs when provisioning a VM.Method 800 begins when there is a need to replicate operators in a firstVM to a second VM (step 810). Thus, if the flow graph in FIG. 7 did notinclude VM2, which contains operators B′, C′ and D′, a streams managermay determine that replicating operators B, C and D in a separate VMwill improve performance of the streaming application. The performancelog is read to determine resource allocation for past implementations ofVM1 that had the best performance (step 820). In other words, theperformance log can include performance data for different VMs that wereimplemented in the past. The resources are allocated to the new VM2according to the past implementation of VM1 that had the bestperformance (step 830). Thus, if the performance log shows thatallocating twice the memory to VM1 that implemented operators B, C and Dresulted in better performance, then the same doubled memory amountcould be allocated to VM2 in step 830. Note the enhancement provided bymethod 800 can be achieved even when a better performing VM must be torndown in step 680 in FIG. 6. The logging of the performance data makesthe streams manager smarter the next time it needs to deploy the sameoperators to a VM in the future.

Note the terminology used herein refers to “tearing down” a VM. This isvernacular that is common and understood by one of ordinary skill in theart to refer to any way to get rid of a VM, which is typically done bystopping the VM and de-allocating the VM's resources back to the cloudmanager, resulting in the VM no longer existing. The tearing down of aVM as used herein is thus deemed to be equivalent to eliminating a VM,destroying a VM, deleting a VM, or any other term that denotes the VM asan entity running on computer hardware ceases to exist. Note also thattearing down a VM can include multiple steps by different entities. Forexample, in FIGS. 4 and 5, the streams manager 360 could tear down a VMby the VM tear down mechanism 522 sending a request to the resourcerequest interface 420 of the cloud provisioning mechanism 410 in thecloud manager 350, which would result in the cloud manager 350 tearingdown the VM.

The disclosure and claims herein relate to a streams manager thatmonitors performance of parallel portions of a streaming applicationimplemented in multiple virtual machines (VMs). When the performanceprovided by the multiple VMs is no longer needed, one or more of the VMscan be torn down. The performance of the VMs is monitored. When theleast performing VM can be torn down, it is torn down. When the leastperforming VM cannot be torn down, information regarding a betterperforming VM is gathered, and it is determined whether the leastperforming VM can be made more similar to the better performing VM. Whenthe least performing VM can be made more similar to the betterperforming VM, the least performing VM is changed to improve itsperformance, and the better performing VM is torn down.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

1. An apparatus comprising: at least one processor; a memory coupled tothe at least one processor; a streaming application residing in thememory and executed by the at least one processor, the streamingapplication comprising a flow graph that includes a plurality ofoperators that process a plurality of data tuples, wherein the flowgraph comprises a first set of operators implemented in a first virtualmachine and a second set of operators implemented in a second virtualmachine, wherein the first set of operators and the second set ofoperators include similar operators and connections between operatorsand operate on data tuples in parallel; and a streams manager residingin the memory and executed by the at least one processor, the streamsmanager monitoring performance of the streaming application, and whenperformance of the streaming application indicates processing datatuples in parallel by the first set of operators and the second set ofoperators is no longer needed, the streams manager determines which ofthe first virtual machine and second virtual machine is leastperforming, and when the least performing virtual machine can be torndown, the streams manager tears down the least performing virtualmachine.
 2. The apparatus of claim 1 wherein when the least performingvirtual machine cannot be torn down, the streams manager gathers dataregarding a better performing virtual machine operating on the flowgraph in parallel with the least performing virtual machine, and whenthe least performing virtual machine can be made more similar to thebetter performing virtual machine, the streams manager makes at leastone change to the least performing virtual machine to improveperformance of the least performing virtual machine and tears down thebetter performing virtual machine.
 3. The apparatus of claim 2 whereinwhen the least performing virtual machine cannot be made more similar tothe better performing virtual machine, the streams manager determineswhen the least performing virtual machine can be migrated to a locationwhere the least performing virtual machine can be made more similar tothe better performing virtual machine, migrates the least performingvirtual machine to the location, makes at least one change to the leastperforming virtual machine to improve performance of the leastperforming virtual machine, and tears down the better performing virtualmachine.
 4. The apparatus of claim 3 wherein when the least performingvirtual machine cannot be migrated, the streams manager tears down thebetter performing virtual machine.
 5. The apparatus of claim 1 whereinthe streams manager logs performance data for each virtual machine thatincludes operators in the flow graph.
 6. The apparatus of claim 5wherein the streams manager uses the logged performance data todetermine a desired configuration for a new virtual machine thatimplements a new set of operators that operate on data tuples inparallel with operators in at least one existing virtual machine.
 7. Theapparatus of claim 1 wherein the streams manager tears down the leastperforming virtual machine by sending a request to a cloud manager totear down the least performing virtual machine.
 8. The apparatus ofclaim 2 wherein the streams manager tears down the better performingvirtual machine by sending a request to a cloud manager to tear down thebetter performing virtual machine.
 9. A computer-implemented methodexecuted by at least one processor for managing a streaming application,the method comprising: executing a streaming application that comprisesa flow graph that includes a plurality of operators that process aplurality of data tuples, wherein the flow graph comprises a first setof operators implemented in a first virtual machine and a second set ofoperators implemented in a second virtual machine, wherein the first setof operators and the second set of operators include similar operatorsand connections between operators and operate on data tuples inparallel; monitoring performance of the streaming application; whenperformance of the streaming application indicates processing datatuples in parallel by the first set of operators and the second set ofoperators is no longer needed, determining which of the first virtualmachine and second virtual machine is least performing; and when theleast performing virtual machine can be torn down, tearing down theleast performing virtual machine.
 10. The method of claim 9 wherein whenthe least performing virtual machine cannot be torn down: gathering dataregarding a better performing virtual machine operating on the flowgraph in parallel with the least performing virtual machine; when theleast performing virtual machine can be made more similar to the betterperforming virtual machine, making at least one change to the leastperforming virtual machine to improve performance of the leastperforming virtual machine; and tearing down the better performingvirtual machine.
 11. The method of claim 10 wherein when the leastperforming virtual machine cannot be made more similar to the betterperforming virtual machine: determining when the least performingvirtual machine can be migrated to a location where the least performingvirtual machine can be made more similar to the better performingvirtual machine; migrating the least performing virtual machine to thelocation; making at least one change to the least performing virtualmachine to improve performance of the least performing virtual machine;and tearing down the better performing virtual machine.
 12. The methodof claim 11 wherein when the least performing virtual machine cannot bemigrated, tearing down the better performing virtual machine.
 13. Themethod of claim 9 further comprising logging performance data for eachvirtual machine that includes operators in the flow graph.
 14. Themethod of claim 13 further comprising using the logged performance datato determine a desired configuration for a new virtual machine thatimplements a new set of operators that operate on data tuples inparallel with operators in at least one existing virtual machine. 15.The method of claim 9 wherein tearing down the least performing virtualmachine comprises sending a request to a cloud manager to tear down theleast performing virtual machine.
 16. The method of claim 10 whereintearing down the better performing virtual machine comprises sending arequest to a cloud manager to tear down the better performing virtualmachine.